Towards Human-like Understanding of Visual Content

While computer vision analyzes the content of images, understanding photographs in the visual media often lies beyond their pure physical content. Artistic and media images make implications that rely on cultural knowledge and appeal to the emotions and of viewers and associations that they make. As a first step in understanding visual media, we focus on the following two tasks: capturing photography style and predicting the authorship of artistic photographs, as well as identifying the intent and bias of the photographer towards their subject. To explore the feasibility of current computer vision techniques to address photographer identification, we created a new dataset of over 180,000 images taken by 41 well-known photographers. Using this dataset, we examined the effectiveness of a variety of features and found that high-level features greatly outperform low-level features for this task. We also use what our method has learned to generate new photographs in the style of an author. In another work, we develop features to predict whether an image portrays a politician in a positive or negative light and as having certain qualities (e.g. competence). We find that the image setting and background have a large impact on how the photograph’s subject is perceived.